Today, many travel brands are in the process of onboarding and improving access to customer data and they often look across the wider plane of marketers to get ideas and best practices.
Yet travel marketers are dealing with a very particular set of behaviors that make it hard to use data the way many other marketers use data.
Travel products can be expensive and purchases are often paid for by someone else (work), or packed with emotion (leisure).
Consumers want travel brands to lean in. In fact, 83% of millennials say they want brands to track their digital patterns, if this would provide them with a more personalized experience.
Because of these differences, travel marketers must step up their game.
Books like Factfulness and Thinking Fast and Slow can teach travel marketers a lot along their journey to data-driven powerhouses.
Both books discuss how much data matters, but also how hard it can be for people to use and interpret data well. Depending on which data you look at, you can see a good story or a bad story, or become misled or confused.
The most successful travel marketer is inspired to up their game with more innovative thinking about data.
More detail required
Most standard ad targeting begins with demographic data about customers. Beyond a certain income level, demographics are a poor indicator of a consumer’s travel behavior.
Take two married women in the same upper middle class zip code. A retailer can be relatively certain that both will buy a few sweaters in the fall. They can focus on a few details like color and size and have a relatively successful “targeted” ad campaign.
Travel companies need much more specific data before they really know enough about a consumer.
One of the women above might travel extensively for work, earn loyalty rewards, and take frequent short domestic holidays with her family.
The other may rarely travel, taking one big trip a year somewhere abroad. The airline marketer would need to discern business from leisure travel, international from domestic preferences, loyalty status, frequency and timing of messaging, and more.
The past doesn’t always predict the future
A recent report illustrates how more companies are starting to use customer lifetime value scores to prioritize customer service, discounts, and other interactions with customers.
These scores, often calculated by third party firms like Optimotive, take demographic information and past spending behavior with the brand into consideration.
For travel brands, as we’ve seen, demographics aren’t enough to calculate lifetime value, but neither is past spending behavior.
Instead, travel brands must use many more unique personal inputs to calculate a more accurate potential future value of an individual. In fact, using a potential value takes new data points into consideration.
For example, someone may travel extensively - with another airline. The current value of that person might seem to be low, but their future value is enormous.
Young adults might have lower incomes than their parents, but many college graduates starting their first jobs will begin traveling much more extensively, and will be signing up for rewards programs for the first time.
These “potential value” inputs should often override the past spending behavior. Why?
Take the example of a hotel mega chain. If it were to prioritize a current high spender over a current low spender using past data only, it would miss the opportunity to delight a newly minted consultant that just got assigned a job in a city they need to travel to four nights every week for the next year.
Every insight is relative
Hans Rosling, the author of Factfulness, likes to explain that data is relative, and that anyone looking at data needs to consider the context.
For example, Thailand gets more than 35 million tourists per year. That’s a lot! Or is it?
In fact, it is a lot. But France, the top destination, gets nearly 90 million, while the average country receives far less.
The strategy team at an international hotel chain looking to expand in Southeast Asia is not going to just look at the total number of tourists that visit a particular country.
They are going to look at the context, whether the number is growing, how much neighboring countries get, and how many hotels already exist.
Marketers should behave the same way.
Travel marketers actually have an ironic advantage over their peers in other industries. Many elements of a customer insight strategy are not yet in place, so they can leap ahead and embrace the most advanced resources available.
It’s likely that a travel or tourism brand has a bucket of valuable customers, such as a resort flagging regular visitors that book suites, or an airline tagging consumers that pay for first class. That’s good to know.
But, perhaps those people actually travel more to another destination, or spend more with a different airline. Perhaps that other resort or airline has a higher average spend overall.
These relative data points are required to put marketing plans in the right context, and also to be relevant when communicating with travelers.
Innovative travel companies, including TripAdvisor, are turning to machine learning to contextualize data, and improve over time.
The largest “pre-transaction” travel website is even tagging all of its community content to create meta-data that will help them create more personalized search results and marketing communication.
Travel marketers actually have an ironic advantage over their peers in other industries.
Adobe reports that only 18% of executives at hospitality and travel brands believe their organization is digitally mature, which is significantly lower than the average.
This means that many elements of a customer insight strategy are not yet in place. Travel marketers can leap ahead and embrace the most advanced resources available.
And they should. Travelers are sophisticated, diverse and, therefore, travel marketing must be, too.